In humans, the knee joint is functionally controlled by a mechanical system governed by three unique types of forces: (1) active forces resulting in motion, such as those resulting from muscle flexing or relaxing; (2) constraining forces that constrain motion, such as those resulting from ligaments being in tension; and (3) compressive forces that resist motion, such as those acting upon bones. In addition to the foregoing bodily tissues accounting for these three forces, cartilage and meniscus also produce a dampening effect dissipating the compressive forces propagating to other joints
Knee joint motions are stabilized primarily by four ligaments, which restrict and regulate the relative motion between the femur, tibia, and patella. These ligaments are the anterior cruciate ligament (ACL), the posterior cruciate ligament (PCL), the medial collateral ligament (MCL), and the lateral collateral ligament (LCL), as shown in FIGS. 1 and 2. An injury to any of these ligaments or other soft-tissue structures can cause detectable changes in knee kinematics and the creation of detectable patterns of vibration representative of the type of knee joint injury and the severity of the injury. These visual and auditory changes are produced by the bones while moving in a distorted kinematic pattern, and they differ significantly from the look and vibration of a properly balanced knee joint moving through a range of motion.
Many research studies have been conducted to assess knee vibration and correlate it with clinical data regarding various joint problems using microphones with or without stethoscope equipment. However, it has been shown that microphones cannot reliably detect joint frequencies, especially those experiencing strong interference from noise, and the signal clearance can substantially influenced by skin friction. It has been hypothesized that the failure associated with the interpretation of sound emissions and possible reasons for occurrence is directly attributable to the complicity of the sound signal, the unknown noise factors, and unknown sound center. It is desirable, therefore, to provide a diagnostic tool that compares patient specific data with kinematic data by providing visual feedback to clinicians.